Package/Library > Sub-package > Modules > Functions, Classes (Attributes, Methods)
Great tutorial: https://docs.python.org/3/tutorial/index.html
Termnal, Console, Shell, CLI: https://www.geeksforgeeks.org/difference-between-terminal-console-shell-and-command-line/
Variables are used to store values. A string is a series of characters, surrounded by single or double quotes.
# Hello world
print("Hello world!")
Hello world!
# Hello world with a variable
msg = "Hello world!"
print(msg)
Hello world!
# f-strings (using variables in strings)
first_name = 'albert'
last_name = 'einstein'
full_name = f"{first_name} {last_name}"
print(full_name)
albert einstein
Another way: Python String format() Method:
Syntax: { }.format(value)
Parameters:
value: Can be an integer, floating point numeric constant, string, characters or even variables.
Returntype: Returns a formatted string with the value passed as parameter in the placeholder position.
A list stores a series of items in a particular order. You access items using an index, or within a loop.
In mathematics, they are mostly used for order of operations. The innermost parentheses are calculated first, followed by the brackets that form the next layer outwards, followed by braces that form a third layer outwards.
# Make a list
bikes = ['trek', 'redline', 'giant']
# Get the first item in a list
first_bike = bikes[0]
first_bike
'trek'
# Looping through a list
for bike in bikes:
print(bike)
trek redline giant
# Adding items to a list
bikes = []
bikes.append('trek')
bikes.append('redline')
bikes.append('giant')
bikes
['trek', 'redline', 'giant']
# Making numerical lists
squares = []
for x in range(1, 11):
squares.append(x**2)
squares
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
# List comprehensions
squares = [x**2 for x in range(1, 11)]
squares
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
# Slicing a list
finishers = ['sam', 'bob', 'ada', 'bea']
first_two = finishers[:2]
first_two
['sam', 'bob']
# Copying a list
copy_of_bikes = bikes[:]
copy_of_bikes
['trek', 'redline', 'giant']
Tuples are similar to lists, but the items in a tuple can't be modified. All the operations for list can be used here too.
# Making a tuple
dimensions = (1920, 1080)
Dictionaries store connections between pieces of information. Each item in a dictionary is a key-value pair.
# A simple dictionary
alien = {'color': 'green', 'points': 5}
# Accessing a value
print(f"The alien's color is {alien['color']}")
The alien's color is green
# Adding a new key-value pair
alien['x_position'] = 0
alien
{'color': 'green', 'points': 5, 'x_position': 0}
# Looping through all key-value pairs
fav_numbers = {'eric': 17, 'ever': 4}
for name, number in fav_numbers.items():
print(f"{name} loves {number}")
eric loves 17 ever loves 4
# Looping through all keys
fav_numbers = {'eric': 17, 'ever': 4}
for name in fav_numbers.keys():
print(f"{name} loves a number")
eric loves a number ever loves a number
# Looping through all the values
fav_numbers = {'eric': 17, 'ever': 4}
for number in fav_numbers.values():
print(f"{number} is a favorite")
17 is a favorite 4 is a favorite
Your programs can prompt the user for input. All input is stored as a string.
# Prompting for a value
name = input("What's your name? ")
print(f"Hello, {name}!")
Hello, Amit!
# Prompting for numerical input
age = input("How old are you? ")
age = int(age)
age
32
pi = input("What's the value of pi? ")
pi = float(pi)
pi
3.0
If statements are used to test for particular conditions and respond appropriately.
# Conditional tests
# equals x == 42
# not equal x != 42
# greater than x > 42
# or equal to x >= 42
# less than x < 42
# or equal to x <= 42
# Conditional test with lists
'trek' in bikes
True
'surly' not in bikes
True
# Assigning boolean values
game_active = True
can_edit = False
# A simple if test
age = 50
if age >= 18:
print("You can vote!")
You can vote!
# If-elif-else statements
if age < 4:
ticket_price = 0
elif age < 18:
ticket_price = 10
else:
ticket_price = 15
ticket_price
15
A while loop repeats a block of code as long as a certain condition is true.
The for statement iterates through a collection or iterable object or generator function. The while statement simply loops until a condition is False. It isn't preference. It's a question of what your data structures are.
Often, we represent the values we want to process as a range (an actual list), or xrange (which generates the values)
(Edit: In Python 3, range is now a generator and behaves like the old xrange function. xrange has been removed from Python 3). This gives us a data structure tailor-made for the for statement.
Generally, however, we have a ready-made collection: a set, tuple, list, map or even a string is already an iterable collection, so we simply use a for loop.
In a few cases, we might want some functional-programming processing done for us, in which case we can apply that transformation as part of iteration. The sorted and enumerate functions apply a transformation on an iterable that fits naturally with the for statement.
If you don't have a tidy data structure to iterate through, or you don't have a generator function that drives your processing, you must use while.
# A simple while loop
current_value = 1
while current_value <= 5:
print(current_value)
current_value += 1
1 2 3 4 5
# Letting the user choose when to quit
msg = ''
while msg != 'quit':
msg = input("What's your message? ")
print(msg)
All is good
quit
Functions are named blocks of code, designed to do one specific job. Information passed to a function is called an argument, and information received by a function is called a parameter.
# A simple function
def greet_user():
"""Display a simple greeting."""
print("Hello!")
greet_user()
Hello!
# Passing an argument
def greet_user(username):
"""Display a personalized greeting."""
print(f"Hello, {username}!")
greet_user('jesse')
Hello, jesse!
# Default values for parameters
def make_pizza(topping='bacon'):
"""Make a single-topping pizza."""
print(f"Have a {topping} pizza!")
make_pizza()
make_pizza('pepperoni')
Have a bacon pizza! Have a pepperoni pizza!
The “int object is not callable” error occurs when you declare a variable and name it with a built-in function name such as int(), sum(), max(), and others.
The error also occurs when you don’t specify an arithmetic operator while performing a mathematical operation.
Do not use the built-in keywords and function names to name variables.
https://www.freecodecamp.org/news/typeerror-int-object-is-not-callable-how-to-fix-in-python
# Sum used as a variable name
def add_numbers(x, y):
"""Add two numbers and return the sum."""
return x + y
sum = add_numbers(3, 5)
print(sum)
# Returning a value
def add_numbers(x, y):
"""Add two numbers and return the sum."""
return x + y
sum_1 = add_numbers(3, 5)
print(sum_1)
8
A class defines the behavior of an object and the kind of information an object can store. The information in a class is stored in attributes, and functions that belong to a class are called methods. A child class inherits the attributes and methods from its parent class.
# Creating a dog class
class Dog():
"""Represent a dog."""
def __init__(self, name):
"""Initialize dog object."""
self.name = name
def sit(self):
"""Simulate sitting."""
print(f"{self.name} is sitting.")
my_dog = Dog('Peso')
print(f"{my_dog.name} is a great dog!")
my_dog.sit()
Peso is a great dog! Peso is sitting.
# Inheritance
class SARDog(Dog):
"""Represent a search dog."""
def __init__(self, name):
"""Initialize the sardog."""
super().__init__(name)
def search(self):
"""Simulate searching."""
print(f"{self.name} is searching.")
my_dog = SARDog('Willie')
print(f"{my_dog.name} is a search dog.")
my_dog.sit()
my_dog.search()
Willie is a search dog. Willie is sitting. Willie is searching.
Your programs can read from files and write to files. Files are opened in read mode ('r') by default, but can also be opened in write mode ('w') and append mode ('a').
Using Fenced Code Blocks with Syntax Highlighting; Test the code in a seperate notebook/terminal as it involves file creation etc. Demo created in 7. Files and Exceptions
Reading a file and storing its lines
filename = 'siddhartha.txt'
with open(filename) as file_object:
lines = file_object.readlines()
for line in lines:
print(line)
Writing to a file
filename = 'journal.txt'
with open(filename, 'w') as file_object:
file_object.write("I love programming.")
Appending to a file
filename = 'journal.txt'
with open(filename, 'a') as file_object:
file_object.write("\nI love making games.")
Exceptions help you respond appropriately to errors that are likely to occur. You place code that might cause an error in the try block. Code that should run in response to an error goes in the except block. Code that should run only if the try block was successful goes in the else block.
# Catching an exception
prompt = "How many tickets do you need? "
num_tickets = input(prompt)
try:
num_tickets = int(num_tickets)
except ValueError:
print("Please try again.")
else:
print("Your tickets are printing.")
Your tickets are printing.
# Catching an exception
prompt = "How many tickets do you need? "
num_tickets = input(prompt)
try:
num_tickets = int(num_tickets)
except ValueError:
print("Please try again.")
else:
print("Your tickets are printing.")
Please try again.
If you had infinite programming skills, what would you build?
As you're learning to program, it's helpful to think about the real-world projects you'd like to create. It's a good habit to keep an "ideas" notebook that you can refer to whenever you want to start a new project. If you haven't done so already, take a few minutes and describe three projects you'd like to create.
Simple is better than complex
If you have a choice between a simple and a complex solution, and both work, use the simple solution. Your code will be easier to maintain, and it will be easier for you and others to build on that code later on.
A list stores a series of items in a particular order. Lists allow you to store sets of information in one place, whether you have just a few items or millions of items. Lists are one of Python's most powerful features readily accessible to new programmers, and they tie together many important concepts in programming.
Use square brackets to define a list, and use commas to separate individual items in the list. Use plural names for lists, to make your code easier to read.
# Making a list
users = ['val', 'bob', 'mia', 'ron', 'ned']
Individual elements in a list are accessed according to their position, called the index. The index of the first element is 0, the index of the second element is 1, and so forth. Negative indices refer to items at the end of the list. To get a particular element, write the name of the list and then the index of the element in square brackets.
# Getting the first element
first_user = users[0]
first_user
'val'
# Getting the second element
second_user = users[1]
# Getting the last element
newest_user = users[-1]
Once you've defined a list, you can change individual elements in the list. You do this by referring to the index of the item you want to modify.
users
['val', 'bob', 'mia', 'ron', 'ned']
# Changing an element
users[0] = 'valerie'
users[-2] = 'ronald'
users
['valerie', 'bob', 'mia', 'ronald', 'ned']
You can add elements to the end of a list, or you can insert them wherever you like in a list.
# Adding an element to the end of the list
users.append('amy')
# Starting with an empty list
users = []
users.append('val')
users.append('bob')
users.append('mia')
users
['val', 'bob', 'mia']
# Inserting elements at a particular position
users.insert(0, 'joe')
users.insert(3, 'bea')
users
['joe', 'val', 'bob', 'bea', 'mia']
You can remove elements by their position in a list, or by the value of the item. If you remove an item by its value, Python removes only the first item that has that value.
# Deleting an element by its position
del users[-1]
users
['joe', 'val', 'bob', 'bea']
# Removing an item by its value
# Just restoring the list
users.append('mia')
users.remove('mia')
users
['joe', 'val', 'bob', 'bea']
If you want to work with an element that you're removing from the list, you can "pop" the element. If you think of the list as a stack of items, pop() takes an item off the top of the stack. By default pop() returns the last element in the list, but you can also pop elements from any position in the list.
# Pop the last item from a list
most_recent_user = users.pop()
print(most_recent_user)
bea
# Pop the first item in a list
first_user = users.pop(0)
print(first_user)
joe
The len() function returns the number of items in a list.
users
['val', 'bob']
# Find the length of a list
num_users = len(users)
print(f"We have {num_users} users.")
We have 2 users.
The sort() method changes the order of a list permanently. The sorted() function returns a copy of the list, leaving the original list unchanged. You can sort the items in a list in alphabetical order, or reverse alphabetical order. You can also reverse the original order of the list. Keep in mind that lowercase and uppercase letters may affect the sort order.
# Creating the list again
users = ['val', 'bob', 'mia', 'ron', 'ned']
# Sorting a list permanently
users.sort()
users
['bob', 'mia', 'ned', 'ron', 'val']
# Sorting a list permanently in reverse alphabetical order
users.sort(reverse=True)
users
['val', 'ron', 'ned', 'mia', 'bob']
# Creating the list again
users = ['val', 'bob', 'mia', 'ron', 'ned']
# Sorting a list temporarily
print(sorted(users))
print(sorted(users, reverse=True))
users
['bob', 'mia', 'ned', 'ron', 'val'] ['val', 'ron', 'ned', 'mia', 'bob']
['val', 'bob', 'mia', 'ron', 'ned']
Note the difference between sort, sorted and reverse
# Creating the list again
users = ['val', 'bob', 'mia', 'ron', 'ned']
# Reversing the order of a list
users.reverse()
users
['ned', 'ron', 'mia', 'bob', 'val']
Lists can contain millions of items, so Python provides an efficient way to loop through all the items in a list. When you set up a loop, Python pulls each item from the list one at a time and stores it in a temporary variable, which you provide a name for. This name should be the singular version of the list name.
The indented block of code makes up the body of the loop, where you can work with each individual item. Any lines that are not indented run after the loop is completed.
# Creating the list again
users = ['val', 'bob', 'mia', 'ron', 'ned']
# Printing all items in a list
for user in users:
print(user)
val bob mia ron ned
# Printing a message for each item, and a separate message afterwards
for user in users:
print(f"Welcome, {user}!")
print("Welcome, we're glad to see you all!")
Welcome, val! Welcome, bob! Welcome, mia! Welcome, ron! Welcome, ned! Welcome, we're glad to see you all!
You can use the range() function to work with a set of numbers efficiently. The range() function starts at 0 by default, and stops one number below the number passed to it. You can use the list() function to efficiently generate a large list of numbers.
# Printing the numbers 0 to 1000
# for number in range(1001):
# print(number)
# Printing the numbers 0 to 5
for number in range(6):
print(number)
0 1 2 3 4 5
# Printing the numbers 1 to 1000
# for number in range(1, 1001):
# print(number)
# Printing the numbers 1 to 5
for number in range(1, 6):
print(number)
1 2 3 4 5
# Making a list of numbers from 1 to a million
numbers = list(range(1, 1000001))
numbers[0:6]
[1, 2, 3, 4, 5, 6]
There are a number of simple statistical operations you can run on a list containing numerical data.
# Finding the minimum value in a list
ages = [93, 99, 66, 17, 85, 1, 35, 82, 2, 77]
youngest = min(ages)
youngest
1
# Finding the maximum value
ages = [93, 99, 66, 17, 85, 1, 35, 82, 2, 77]
oldest = max(ages)
oldest
99
# Finding the sum of all values
ages = [93, 99, 66, 17, 85, 1, 35, 82, 2, 77]
total_years = sum(ages)
total_years
557
You can work with any set of elements from a list. A portion of a list is called a slice. To slice a list start with the index of the first item you want, then add a colon and the index after the last item you want. Leave off the first index to start at the beginning of the list, and leave off the last index to slice through the end of the list.
# Getting the first three items
finishers = ['kai', 'abe', 'ada', 'gus', 'zoe']
first_three = finishers[:3]
first_three
['kai', 'abe', 'ada']
# Getting the middle three items
middle_three = finishers[1:4]
middle_three
['abe', 'ada', 'gus']
# Getting the last three items
last_three = finishers[-3:]
last_three
['ada', 'gus', 'zoe']
To copy a list make a slice that starts at the first item and ends at the last item. If you try to copy a list without using this approach, whatever you do to the copied list will affect the original list as well.
# Making a copy of a list
finishers = ['kai', 'abe', 'ada', 'gus', 'zoe']
copy_of_finishers = finishers[:]
copy_of_finishers
['kai', 'abe', 'ada', 'gus', 'zoe']
You can use a loop to generate a list based on a range of numbers or on another list. This is a common operation, so Python offers a more efficient way to do it. List comprehensions may look complicated at first; if so, use the for loop approach until you're ready to start using comprehensions.
To write a comprehension, define an expression for the values you want to store in the list. Then write a for loop to generate input values needed to make the list.
# Using a loop to generate a list of square numbers
squares = []
for x in range(1, 11):
square = x**2
squares.append(square)
squares
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
# Using a comprehension to generate a list of square numbers
squares = [x**2 for x in range(1, 11)]
squares
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
# Using a loop to convert a list of names to upper case
names = ['kai', 'abe', 'ada', 'gus', 'zoe']
upper_names = []
for name in names:
upper_names.append(name.upper())
upper_names
['KAI', 'ABE', 'ADA', 'GUS', 'ZOE']
# Using a comprehension to convert a list of names to upper case
names = ['kai', 'abe', 'ada', 'gus', 'zoe']
upper_names = [name.upper() for name in names]
upper_names
['KAI', 'ABE', 'ADA', 'GUS', 'ZOE']
A tuple is like a list, except you can't change the values in a tuple once it's defined. Tuples are good for storing information that shouldn't be changed throughout the life of a program. Tuples are usually designated by parentheses. (You can overwrite an entire tuple, but you can't change the individual elements in a tuple.)
All activities of Lists are applicable for Tuples except some.
# Defining a tuple
dimensions = (800, 600)
# Looping through a tuple
for dimension in dimensions:
print(dimension)
800 600
# Overwriting a tuple
dimensions = (800, 600)
print(dimensions)
dimensions = (1200, 900)
dimensions
(800, 600)
(1200, 900)
dimensions[0:2]
(1200, 900)
Readability counts:
When you're first learning about data structures such as lists, it helps to visualize how Python is working with the information in your program. pythontutor.com is a great tool for seeing how Python keeps track of the information in a list. Try running the following code on pythontutor.com, and then run your own code.
# Build a list and print the items in the list
dogs = []
dogs.append('willie')
dogs.append('hootz')
dogs.append('peso')
dogs.append('goblin')
for dog in dogs:
print(f"Hello {dog}!")
print("I love these dogs!")
print("\nThese were my first two dogs:")
old_dogs = dogs[:2]
for old_dog in old_dogs:
print(old_dog)
print()
del dogs[0]
dogs.remove('peso')
print(dogs)
Hello willie! Hello hootz! Hello peso! Hello goblin! I love these dogs! These were my first two dogs: willie hootz ['hootz', 'goblin']
Python's dictionaries allow you to connect pieces of related information. Each piece of information in a dictionary is stored as a key-value pair. When you provide a key, Python returns the value associated with that key. You can loop through all the key-value pairs, all the keys, or all the values.
Use curly braces to define a dictionary. Use colons to connect keys and values, and use commas to separate individual key-value pairs.
# Making a dictionary
alien_0 = {'color': 'green', 'points': 5}
To access the value associated with an individual key give the name of the dictionary and then place the key in a set of square brackets. If the key you're asking for is not in the dictionary, an error will occur.
You can also use the get() method, which returns None instead of an error if the key doesn't exist. You can also specify a default value to use if the key is not in the dictionary.
# Getting the value associated with a key
alien_0 = {'color': 'green', 'points': 5}
print(alien_0['color'])
print(alien_0['points'])
green 5
# Getting the value with get()
alien_0 = {'color': 'green'}
alien_color = alien_0.get('color')
# 'points' doesn't exist in alien_0; 0 default value is assigned
alien_points = alien_0.get('points', 0)
# 'points' doesn't exist in alien_0; No default value is assigned
alien_points_none = alien_0.get('points')
print(alien_color)
print(alien_points)
print(alien_points_none)
green 0 None
You can store as many key-value pairs as you want in a dictionary, until your computer runs out of memory. To add a new key-value pair to an existing dictionary give the name of the dictionary and the new key in square brackets, and set it equal to the new value.
This also allows you to start with an empty dictionary and add key-value pairs as they become relevant.
# Adding a key-value pair
alien_0 = {'color': 'green', 'points': 5}
alien_0['x'] = 0
alien_0['y'] = 25
alien_0['speed'] = 1.5
alien_0
{'color': 'green', 'points': 5, 'x': 0, 'y': 25, 'speed': 1.5}
# Adding to an empty dictionary
alien_0 = {}
alien_0['color'] = 'green'
alien_0['points'] = 5
alien_0
{'color': 'green', 'points': 5}
You can modify the value associated with any key in a dictionary. To do so give the name of the dictionary and enclose the key in square brackets, then provide the new value for that key.
# Modifying values in a dictionary
alien_0 = {'color': 'green', 'points': 5}
print(alien_0)
# Change the alien's color and point value.
alien_0['color'] = 'yellow'
alien_0['points'] = 10
print(alien_0)
{'color': 'green', 'points': 5}
{'color': 'yellow', 'points': 10}
You can remove any key-value pair you want from a dictionary. To do so use the del keyword and the dictionary name, followed by the key in square brackets. This will delete the key and its associated value.
# Deleting a key-value pair
alien_0 = {'color': 'green', 'points': 5}
print(alien_0)
del alien_0['points']
print(alien_0)
{'color': 'green', 'points': 5}
{'color': 'green'}
You can loop through a dictionary in three ways: you can loop through all the key-value pairs, all the keys, or all the values.
Dictionaries keep track of the order in which key-value pairs are added. If you want to process the information in a different order, you can sort the keys in your loop.
# Looping through all key-value pairs
# Store people's favorite languages.
fav_languages = {
'jen': 'python',
'sarah': 'c',
'edward': 'ruby',
'phil': 'python',
}
# Show each person's favorite language.
for name, language in fav_languages.items():
print(f"{name}: {language}")
jen: python sarah: c edward: ruby phil: python
# Looping through all the keys
# Show everyone who's taken the survey.
for name in fav_languages.keys():
print(name)
jen sarah edward phil
# Looping through all the values
# Show all the languages that have been chosen.
for language in fav_languages.values():
print(language)
python c ruby python
# Looping through all the keys in reverse order
# Show each person's favorite language,
# in reverse order by the person's name.
for name, language in sorted(fav_languages.items(), reverse=True):
print(f"{name}: {language}")
sarah: c phil: python jen: python edward: ruby
You can find the number of key-value pairs in a dictionary.
# Finding a dictionary's length
num_responses = len(fav_languages)
num_responses
4
It's sometimes useful to store a set of dictionaries in a list; this is called nesting.
# Storing dictionaries in a list
# Two for loops in the code:
# 1. Referring to the Two Dictionaries in the List for user_dict in users:
# 2. Referring to the Items in the Two Dictionaries for k, v in user_dict.items():
# Start with an empty list.
users = []
# Make a new user, and add them to the list.
new_user = {
'last': 'fermi',
'first': 'enrico',
'username': 'efermi',
}
users.append(new_user)
# Make another new user, and add them as well.
new_user = {
'last': 'curie',
'first': 'marie',
'username': 'mcurie',
}
users.append(new_user)
# Understand the users
print(users)
print("\n")
# Show all information about each user.
for user_dict in users:
for k, v in user_dict.items():
print(f"{k}: {v}")
print("\n")
[{'last': 'fermi', 'first': 'enrico', 'username': 'efermi'}, {'last': 'curie', 'first': 'marie', 'username': 'mcurie'}]
last: fermi
first: enrico
username: efermi
last: curie
first: marie
username: mcurie
# You can also define a list of dictionaries directly, without using append():
# Define a list of users, where each user
# is represented by a dictionary.
users = [
{
'last': 'fermi',
'first': 'enrico',
'username': 'efermi',
},
{
'last': 'curie',
'first': 'marie',
'username': 'mcurie',
},
]
# Understand the users
print(users)
print("\n")
# Show all information about each user.
for user_dict in users:
for k, v in user_dict.items():
print(f"{k}: {v}")
print("\n")
[{'last': 'fermi', 'first': 'enrico', 'username': 'efermi'}, {'last': 'curie', 'first': 'marie', 'username': 'mcurie'}]
last: fermi
first: enrico
username: efermi
last: curie
first: marie
username: mcurie
Storing a list inside a dictionary allows you to associate more than one value with each key.
# Storing lists in a dictionary
# Store multiple languages for each person.
fav_languages = {
'jen': ['python', 'ruby'],
'sarah': ['c'],
'edward': ['ruby', 'go'],
'phil': ['python', 'haskell'],
}
# Show all responses for each person.
for name, langs in fav_languages.items():
print(f"{name}: ")
for lang in langs:
print(f"- {lang}")
print("\n")
jen: - python - ruby sarah: - c edward: - ruby - go phil: - python - haskell
You can store a dictionary inside another dictionary. In this case each value associated with a key is itself a dictionary.
# Storing dictionaries in a dictionary
users = {
'aeinstein': {
'first': 'albert',
'last': 'einstein',
'location': 'princeton',
},
'mcurie': {
'first': 'marie',
'last': 'curie',
'location': 'paris',
},
}
for username, user_dict in users.items():
print("\nUsername: " + username)
full_name = user_dict['first'] + " "
full_name += user_dict['last']
location = user_dict['location']
print(f"\tFull name: {full_name.title()}")
print(f"\tLocation: {location.title()}")
Username: aeinstein Full name: Albert Einstein Location: Princeton Username: mcurie Full name: Marie Curie Location: Paris
Nesting is extremely useful in certain situations. However, be aware of making your code overly complex. If you're nesting items much deeper than what you see here there are probably simpler ways of managing your data, such as using classes.
A comprehension is a compact way of generating a dictionary, similar to a list comprehension. To make a dictionary comprehension, define an expression for the key-value pairs you want to make. Then write a for loop to generate the values that will feed into this expression.
The zip() function matches each item in one list to each item in a second list. It can be used to make a dictionary from two lists.
# Using loop to make a dictionary
squares = {}
for x in range(5):
squares[x] = x**2
squares
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
# Using a dictionary comprehension
squares = {x:x**2 for x in range(5)}
squares
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
# Using zip() to make a dictionary
group_1 = ['kai', 'abe', 'ada', 'gus', 'zoe']
group_2 = ['jen', 'eva', 'dan', 'isa', 'meg']
pairings = {name:name_2 for name, name_2 in zip(group_1, group_2)}
pairings
{'kai': 'jen', 'abe': 'eva', 'ada': 'dan', 'gus': 'isa', 'zoe': 'meg'}
You can use a loop to generate a large number of dictionaries efficiently, if all the dictionaries start out with similar data.
# A million aliens
aliens = []
# Make a million green aliens, worth 5 points
# each. Have them all start in one row.
for alien_num in range(1000000):
new_alien = {}
new_alien['color'] = 'green'
new_alien['points'] = 5
new_alien['x'] = 20 * alien_num
new_alien['y'] = 0
aliens.append(new_alien)
# Prove the list contains a million aliens.
num_aliens = len(aliens)
print(f"Number of aliens created: {num_aliens}")
# Some aliens from the list
aliens[0:3]
Number of aliens created: 1000000
[{'color': 'green', 'points': 5, 'x': 0, 'y': 0},
{'color': 'green', 'points': 5, 'x': 20, 'y': 0},
{'color': 'green', 'points': 5, 'x': 40, 'y': 0}]
Try running some of these examples on pythontutor.com.
If statements allow you to examine the current state of a program and respond appropriately to that state. You can write a simple if statement that checks one condition, or you can create a complex series of if statements that identify the exact conditions you're looking for.
While loops run as long as certain conditions remain true. You can use while loops to let your programs run as long as your users want them to.
A conditional test is an expression that can be evaluated as True or False. Python uses the values True and False to decide whether the code in an if statement should be executed.
# Checking for equality
# A single equal sign assigns a value to a variable. A double equal
# sign (==) checks whether two values are equal.
car = 'bmw'
car == 'bmw'
True
car = 'audi'
car == 'bmw'
False
# Ignoring case when making a comparison
car = 'Audi'
car.lower() == 'audi'
True
# Checking for inequality
topping = 'mushrooms'
topping != 'anchovies'
True
Testing numerical values is similar to testing string values.
# Testing equality and inequality
age = 18
age == 18
True
age != 18
False
# Comparison operators
age = 19
age < 21
True
age <= 21
True
age > 21
False
age >= 21
False
You can check multiple conditions at the same time. The and operator returns True if all the conditions listed are True. The or operator returns True if any condition is True.
# Using and to check multiple conditions
age_0 = 22
age_1 = 18
age_0 >= 21 and age_1 >= 21
False
age_1 = 23
age_0 >= 21 and age_1 >= 21
True
# Using or to check multiple conditions
age_0 = 22
age_1 = 18
age_0 >= 21 or age_1 >= 21
True
age_0 = 18
age_0 >= 21 or age_1 >= 21
False
A boolean value is either True or False. Variables with boolean values are often used to keep track of certain conditions within a program.
# Simple boolean values
game_active = True
can_edit = False
Several kinds of if statements exist. Your choice of which to use depends on the number of conditions you need to test. You can have as many elif blocks as you need, and the else block is always optional.
# Simple if statement
age = 19
if age >= 18:
print("You're old enough to vote!")
You're old enough to vote!
# If-else statements
age = 17
if age >= 18:
print("You're old enough to vote!")
else:
print("You can't vote yet.")
You can't vote yet.
# The if-elif-else chain
age = 12
if age < 4:
price = 0
elif age < 18:
price = 5
else:
price = 10
print(f"Your cost is ${price}.")
Your cost is $5.
You can easily test whether a certain value is in a list. You can also test whether a list is empty before trying to loop through the list.
# Testing if a value is in a list
players = ['al', 'bea', 'cyn', 'dale']
'al' in players
True
'eric' in players
False
# Testing if a value is not in a list
banned_users = ['ann', 'chad', 'dee']
user = 'erin'
if user not in banned_users:
print("You can play!")
You can play!
# Checking if a list is empty
players = []
if players:
for player in players:
print(f"Player: {player.title()}")
else:
print("We have no players yet!")
We have no players yet!
You can allow your users to enter input using the input() statement. All input is initially stored as a string. If you want to accept numerical input, you'll need to convert the input string value to a numerical type.
# Simple input
name = input("What's your name? ")
print(f"Hello, {name}.")
Hello, Amit.
# Accepting numerical input using int()
age = input("How old are you? ")
age = int(age)
if age >= 18:
print("\nYou can vote!")
else:
print("\nYou can't vote yet.")
You can vote!
# Accepting numerical input using float()
tip = input("How much do you want to tip? ")
tip = float(tip)
A while loop repeats a block of code as long as a condition is True.
# Counting to 5
current_number = 1
while current_number <= 5:
print(current_number)
current_number += 1
1 2 3 4 5
# Letting the user choose when to quit
prompt = "\nTell me something, and I'll "
prompt += "repeat it back to you."
prompt += "\nEnter 'quit' to end the program. "
message = ""
while message != 'quit':
message = input(prompt)
if message != 'quit':
print(message)
# Using a flag
prompt = "\nTell me something, and I'll "
prompt += "repeat it back to you."
prompt += "\nEnter 'quit' to end the program. "
active = True
while active:
message = input(prompt)
if message == 'quit':
active = False
else:
print(message)
All is good
# Using break to exit a loop
prompt = "\nWhat cities have you visited?"
prompt += "\nEnter 'quit' when you're done. "
while True:
city = input(prompt)
if city == 'quit':
break
else:
print(f"I've been to {city}!")
I've been to Bang!
You can use the break statement and the continue statement with any of Python's loops. For example you can use break to quit a for loop that's working through a list or a dictionary. You can use continue to skip over certain items when looping through a list or dictionary as well.
# Using continue in a loop
banned_users = ['eve', 'fred', 'gary', 'helen']
prompt = "\nAdd a player to your team."
prompt += "\nEnter 'quit' when you're done. "
players = []
while True:
player = input(prompt)
if player == 'quit':
break
elif player in banned_users:
print(f"{player} is banned!")
continue
else:
players.append(player)
print("\nYour team:")
for player in players:
print(player)
Your team: Amit Miss X MPS
The remove() method removes a specific value from a list, but it only removes the first instance of the value you provide. You can use a while loop to remove all instances of a particular value.
# Removing all cats from a list of pets
pets = ['dog', 'cat', 'dog', 'fish', 'cat',
'rabbit', 'cat']
print(pets)
while 'cat' in pets:
pets.remove('cat')
print(pets)
['dog', 'cat', 'dog', 'fish', 'cat', 'rabbit', 'cat'] ['dog', 'dog', 'fish', 'rabbit']
Every while loop needs a way to stop running so it won't continue to run forever. If there's no way for the condition to become False, the loop will never stop running. You can usually press Ctrl-C to stop an infinite loop.
You can press I twice to interrupt the kernel. This only works if you're in Command mode. If not already enabled, press Esc to enable it.
An infinite loop; Test in a seperate notebook
while True:
name = input("\nWho are you? ")
print(f"Nice to meet you, {name}!")
Sublime Text doesn't run programs that prompt the user for input. You can use Sublime Text to write programs that prompt for input, but you'll need to run these programs from a terminal.
Functions are named blocks of code designed to do one specific job. Functions allow you to write code once that can then be run whenever you need to accomplish the same task. Functions can take in the information they need, and return the information they generate. Using functions effectively makes your programs easier to write, read, test, and fix.
The first line of a function is its definition, marked by the keyword def. The name of the function is followed by a set of parentheses and a colon. A docstring, in triple quotes, describes what the function does. The body of a function is indented one level.
To call a function, give the name of the function followed by a set of parentheses.
# Making a function
def greet_user():
"""Display a simple greeting."""
print("Hello!")
greet_user()
Hello!
Information that's passed to a function is called an argument; information that's received by a function is called a parameter. Arguments are included in parentheses after the function's name, and parameters are listed in parentheses in the function's definition.
# Passing a single argument
def greet_user(username):
"""Display a simple greeting."""
print(f"Hello, {username}!")
greet_user('jesse')
greet_user('diana')
greet_user('brandon')
Hello, jesse! Hello, diana! Hello, brandon!
The two main kinds of arguments are positional and keyword arguments. When you use positional arguments Python matches the first argument in the function call with the first parameter in the function definition, and so forth.
With keyword arguments, you specify which parameter each argument should be assigned to in the function call. When you use keyword arguments, the order of the arguments doesn't matter.
The variables that are defined when the function is declared are known as a parameter
Whereas
The values that are declared within a function when the function is called are known as an argument.
Function = PA = Parameter/Define > Argument/Call
# Using positional arguments
def describe_pet(animal, name):
"""Display information about a pet."""
print(f"\nI have a {animal}.")
print(f"Its name is {name}.")
describe_pet('hamster', 'harry')
describe_pet('dog', 'willie')
I have a hamster. Its name is harry. I have a dog. Its name is willie.
# Using keyword arguments
def describe_pet(animal, name):
"""Display information about a pet."""
print(f"\nI have a {animal}.")
print(f"Its name is {name}.")
describe_pet(animal='hamster', name='harry')
describe_pet(name='willie', animal='dog')
I have a hamster. Its name is harry. I have a dog. Its name is willie.
You can provide a default value for a parameter. When function calls omit this argument the default value will be used. Parameters with default values must be listed after parameters without default values in the function's definition so positional arguments can still work correctly.
# Using a default value
def describe_pet(name, animal='dog'):
"""Display information about a pet."""
print(f"\nI have a {animal}.")
print(f"Its name is {name}.")
describe_pet('harry', 'hamster')
describe_pet('willie')
I have a hamster. Its name is harry. I have a dog. Its name is willie.
# Using None to make an argument optional
def describe_pet(animal, name=None):
"""Display information about a pet."""
print(f"\nI have a {animal}.")
if name:
print(f"Its name is {name}.")
describe_pet('hamster', 'harry')
describe_pet('snake')
I have a hamster. Its name is harry. I have a snake.
A function can return a value or a set of values. When a function returns a value, the calling line should provide a variable which the return value can be assigned to. A function stops running when it reaches a return statement.
# Returning a single value
def get_full_name(first, last):
"""Return a neatly formatted full name."""
full_name = f"{first} {last}"
return full_name.title()
musician = get_full_name('jimi', 'hendrix')
print(musician)
Jimi Hendrix
# Returning a dictionary
def build_person(first, last):
"""Return a dictionary of information
about a person.
"""
person = {'first': first, 'last': last}
return person
musician = build_person('jimi', 'hendrix')
print(musician)
{'first': 'jimi', 'last': 'hendrix'}
# Returning a dictionary with optional values
def build_person(first, last, age=None):
"""Return a dictionary of information
about a person.
"""
person = {'first': first, 'last': last}
if age:
person['age'] = age
return person
musician = build_person('jimi', 'hendrix', 27)
print(musician)
musician = build_person('janis', 'joplin')
print(musician)
{'first': 'jimi', 'last': 'hendrix', 'age': 27}
{'first': 'janis', 'last': 'joplin'}
You can pass a list as an argument to a function, and the function can work with the values in the list. Any changes the function makes to the list will affect the original list. You can prevent a function from modifying a list by passing a copy of the list as an argument.
# Passing a list as an argument
def greet_users(names):
"""Print a simple greeting to everyone."""
for name in names:
msg = f"Hello, {name}!"
print(msg)
usernames = ['hannah', 'ty', 'margot']
greet_users(usernames)
Hello, hannah! Hello, ty! Hello, margot!
# Allowing a function to modify a list
# The following example sends a list of models to a function for
# printing. The original list is emptied, and the second list is filled.
def print_models(unprinted, printed):
"""3d print a set of models."""
while unprinted:
current_model = unprinted.pop()
print(f"Printing {current_model}")
printed.append(current_model)
# Store some unprinted designs,
# and print each of them.
unprinted = ['phone case', 'pendant', 'ring']
printed = []
print_models(unprinted, printed)
print(f"\nUnprinted: {unprinted}")
print(f"Printed: {printed}")
Printing ring Printing pendant Printing phone case Unprinted: [] Printed: ['ring', 'pendant', 'phone case']
# Preventing a function from modifying a list
# The following example is the same as the previous one, except the
# original list is unchanged after calling print_models().
def print_models(unprinted, printed):
"""3d print a set of models."""
while unprinted:
current_model = unprinted.pop()
print(f"Printing {current_model}")
printed.append(current_model)
# Store some unprinted designs,
# and print each of them.
original = ['phone case', 'pendant', 'ring']
printed = []
print_models(original[:], printed)
print(f"\nOriginal: {original}")
print(f"Printed: {printed}")
Printing ring Printing pendant Printing phone case Original: ['phone case', 'pendant', 'ring'] Printed: ['ring', 'pendant', 'phone case']
Sometimes you won't know how many arguments a function will need to accept. Python allows you to collect an arbitrary number of arguments into one parameter using the * operator. A parameter that accepts an arbitrary number of arguments must come last in the function definition.
The ** operator allows a parameter to collect an arbitrary number of keyword arguments. These arguments are stored as a dictionary with the parameter names as keys, and the arguments as values. Collecting an arbitrary number
# Collecting an arbitrary number of positional arguments
def make_pizza(size, *toppings):
"""Make a pizza."""
print(f"\nMaking a {size} pizza.")
print("Toppings:")
for topping in toppings:
print(f"- {topping}")
# Make three pizzas with different toppings.
make_pizza('small', 'pepperoni')
make_pizza('large', 'bacon bits', 'pineapple')
make_pizza('medium', 'mushrooms', 'peppers', 'onions', 'extra cheese')
Making a small pizza. Toppings: - pepperoni Making a large pizza. Toppings: - bacon bits - pineapple Making a medium pizza. Toppings: - mushrooms - peppers - onions - extra cheese
# Collecting an arbitrary number of keyword arguments
def build_profile(first, last, **user_info):
"""Build a dictionary for a user."""
user_info['first'] = first
user_info['last'] = last
return user_info
# Create two users with different kinds
# of information.
user_0 = build_profile('albert', 'einstein', location='princeton')
user_1 = build_profile('marie', 'curie', location='paris', field='chemistry')
print(user_0)
print(user_1)
{'location': 'princeton', 'first': 'albert', 'last': 'einstein'}
{'location': 'paris', 'field': 'chemistry', 'first': 'marie', 'last': 'curie'}
You can store your functions in a separate file called a module, and then import the functions you need into the file containing your main program. This allows for cleaner program files. (Make sure your module is stored in the same directory as your main program.)
Note the Dot notation used to access the Function.
Storing a function in a module File: pizza.py
def make_pizza(size, *toppings):
"""Make a pizza."""
print(f"\nMaking a {size} pizza.")
print("Toppings:")
for topping in toppings:
print(f"- {topping}")
# File: making_pizzas.py; File using the pizza.py module; Check the working directory for the same
# Importing an entire module
# Every function in the module is available in the program file.
import pizza
pizza.make_pizza('medium', 'pepperoni')
pizza.make_pizza('small', 'bacon', 'pineapple')
Making a medium pizza. Toppings: - pepperoni Making a small pizza. Toppings: - bacon - pineapple
# Importing a specific function
# Only the imported functions are available in the program file.
from pizza import make_pizza
make_pizza('medium', 'pepperoni')
make_pizza('small', 'bacon', 'pineapple')
Making a medium pizza. Toppings: - pepperoni Making a small pizza. Toppings: - bacon - pineapple
# Giving a module an alias
import pizza as p
p.make_pizza('medium', 'pepperoni')
p.make_pizza('small', 'bacon', 'pineapple')
Making a medium pizza. Toppings: - pepperoni Making a small pizza. Toppings: - bacon - pineapple
# Giving a function an alias
from pizza import make_pizza as mp
mp('medium', 'pepperoni')
mp('small', 'bacon', 'pineapple')
Making a medium pizza. Toppings: - pepperoni Making a small pizza. Toppings: - bacon - pineapple
Importing all functions from a module: Don't do this, but recognize it when you see it in others' code. It can result in naming conflicts, which can cause errors.
from pizza import *
make_pizza('medium', 'pepperoni')
make_pizza('small', 'bacon', 'pineapple')
Making a medium pizza. Toppings: - pepperoni Making a small pizza. Toppings: - bacon - pineapple
As you can see there are many ways to write and call a function. When you're starting out, aim for something that simply works. As you gain experience you'll develop an understanding of the more subtle advantages of different structures such as positional and keyword arguments, and the various approaches to importing functions. For now if your functions do what you need them to, you're doing well.
Try running some of these examples on pythontutor.com
Class > Objects/Instances > Attributes/Properties, Methods/Functions
Classes are the foundation of object-oriented programming. Classes represent real-world things you want to model in your programs: for example dogs, cars, and robots. You use a class to make objects, which are specific instances of dogs, cars, and robots. A class defines the general behavior that a whole category of objects can have, and the information that can be associated with those objects.
Classes can inherit from each other – you can write a class that extends the functionality of an existing class. This allows you to code efficiently for a wide variety of situations.
In Python class names are written in CamelCase and object names are written in lowercase with underscores. Modules that contain classes should be named in lowercase with underscores.
There are many ways to model real world objects and situations in code, and sometimes that variety can feel overwhelming. Pick an approach and try it – if your first attempt doesn't work, try a different approach.
Consider how we might model a car. What information would we associate with a car, and what behavior would it have? The information is stored in variables called attributes, and the behavior is represented by functions.
Functions that are part of a class are called methods.
# The Car class
class Car:
"""A simple attempt to model a car."""
def __init__(self, make, model, year):
"""Initialize car attributes."""
self.make = make
self.model = model
self.year = year
# Fuel capacity and level in gallons.
self.fuel_capacity = 15
self.fuel_level = 0
def fill_tank(self):
"""Fill gas tank to capacity."""
self.fuel_level = self.fuel_capacity
print("Fuel tank is full.")
def drive(self):
"""Simulate driving."""
print("The car is moving.")
# Creating an object from a class
my_car = Car('audi', 'a4', 2016)
# Accessing attribute values
print(my_car.make)
print(my_car.model)
print(my_car.year)
audi a4 2016
# Calling methods
my_car.fill_tank()
my_car.drive()
Fuel tank is full. The car is moving.
# Creating multiple objects
my_car = Car('audi', 'a4', 2019)
my_old_car = Car('subaru', 'outback', 2015)
my_truck = Car('toyota', 'tacoma', 2012)
You can modify an attribute's value directly, or you can write methods that manage updating values more carefully.
# Modifying an attribute directly
my_new_car = Car('audi', 'a4', 2019)
my_new_car.fuel_level = 5
# Writing a method to update an attribute's value
def update_fuel_level(self, new_level):
"""Update the fuel level."""
if new_level <= self.fuel_capacity:
self.fuel_level = new_level
else:
print("The tank can't hold that much!")
# Writing a method to increment an attribute's value
def add_fuel(self, amount):
"""Add fuel to the tank."""
if (self.fuel_level + amount <= self.fuel_capacity):
self.fuel_level += amount
print("Added fuel.")
else:
print("The tank won't hold that much.")
If the class you're writing is a specialized version of another class, you can use inheritance. When one class inherits from another, it automatically takes on all the attributes and methods of the parent class. The child class is free to introduce new attributes and methods, and override attributes and methods of the parent class.
To inherit from another class include the name of the parent class in parentheses when defining the new class.
# The __init__() method for a child class
class ElectricCar(Car):
"""A simple model of an electric car."""
def __init__(self, make, model, year):
"""Initialize an electric car."""
super().__init__(make, model, year)
# Attributes specific to electric cars.
# Battery capacity in kWh.
self.battery_size = 75
# Charge level in %.
self.charge_level = 0
# Adding new methods to the child class
class ElectricCar(Car):
"""A simple model of an electric car."""
def __init__(self, make, model, year):
"""Initialize an electric car."""
super().__init__(make, model, year)
# Attributes specific to electric cars.
# Battery capacity in kWh.
self.battery_size = 75
# Charge level in %.
self.charge_level = 0
def charge(self):
"""Fully charge the vehicle."""
self.charge_level = 100
print("The vehicle is fully charged.")
# Using child methods and parent methods
my_ecar = ElectricCar('tesla', 'model s', 2019)
my_ecar.charge() # From the child class
my_ecar.drive() # From the parent class
The vehicle is fully charged. The car is moving.
# Overriding parent methods
class ElectricCar(Car):
"""A simple model of an electric car."""
def __init__(self, make, model, year):
"""Initialize an electric car."""
super().__init__(make, model, year)
# Attributes specific to electric cars.
# Battery capacity in kWh.
self.battery_size = 75
# Charge level in %.
self.charge_level = 0
def charge(self):
"""Fully charge the vehicle."""
self.charge_level = 100
print("The vehicle is fully charged.")
def fill_tank(self): # Method from parent class
"""Display an error message."""
print("This car has no fuel tank!")
A class can have objects as attributes. This allows classes to work together to model complex situations.
# A Battery class
class Battery:
"""A battery for an electric car."""
def __init__(self, size=75):
"""Initialize battery attributes."""
# Capacity in kWh, charge level in %.
self.size = size
self.charge_level = 0
def get_range(self):
"""Return the battery's range."""
if self.size == 75:
return 260
elif self.size == 100:
return 315
# Using an instance as an attribute
class ElectricCar(Car):
"""A battery for an electric car.""" # Using the Battery Class
def __init__(self, size=75):
"""Initialize battery attributes."""
# Capacity in kWh, charge level in %.
self.size = size
self.charge_level = 0
def get_range(self):
"""Return the battery's range."""
if self.size == 75:
return 260
elif self.size == 100:
return 315
def __init__(self, make, model, year):
"""Initialize an electric car."""
super().__init__(make, model, year)
# Attribute specific to electric cars.
self.battery = Battery() # Attribute = Instance; From the Battery Class
def charge(self):
"""Fully charge the vehicle."""
self.battery.charge_level = 100 # Using the attributes from the Battery Class
print("The vehicle is fully charged.")
# Using the instance
my_ecar = ElectricCar('tesla', 'model x', 2019)
my_ecar.charge()
print(my_ecar.battery.get_range())
my_ecar.drive()
The vehicle is fully charged. 260 The car is moving.
Class files can get long as you add detailed information and functionality. To help keep your program files uncluttered, you can store your classes in modules and import the classes you need into your main program.
Storing classes in a file called car.py
# The Car class
class Car:
"""A simple attempt to model a car."""
def __init__(self, make, model, year):
"""Initialize car attributes."""
self.make = make
self.model = model
self.year = year
# Fuel capacity and level in gallons.
self.fuel_capacity = 15
self.fuel_level = 0
def fill_tank(self):
"""Fill gas tank to capacity."""
self.fuel_level = self.fuel_capacity
print("Fuel tank is full.")
def drive(self):
"""Simulate driving."""
print("The car is moving.")
# A Battery class
class Battery:
"""A battery for an electric car."""
def __init__(self, size=75):
"""Initialize battery attributes."""
# Capacity in kWh, charge level in %.
self.size = size
self.charge_level = 0
def get_range(self):
"""Return the battery's range."""
if self.size == 75:
return 260
elif self.size == 100:
return 315
# Using an instance as an attribute
class ElectricCar(Car):
"""A battery for an electric car."""
def __init__(self, size=75):
"""Initialize battery attributes."""
# Capacity in kWh, charge level in %.
self.size = size
self.charge_level = 0
def get_range(self):
"""Return the battery's range."""
if self.size == 75:
return 260
elif self.size == 100:
return 315
def __init__(self, make, model, year):
"""Initialize an electric car."""
super().__init__(make, model, year)
# Attribute specific to electric cars.
self.battery = Battery()
def charge(self):
"""Fully charge the vehicle."""
self.battery.charge_level = 100
print("The vehicle is fully charged.")
# Importing individual classes from a module File: my_cars.py
from car import Car, ElectricCar
my_beetle = Car('volkswagen', 'beetle', 2016)
my_beetle.fill_tank()
my_beetle.drive()
my_tesla = ElectricCar('tesla', 'model s', 2016)
my_tesla.charge()
my_tesla.drive()
Fuel tank is full. The car is moving. The vehicle is fully charged. The car is moving.
# Importing an entire module
import car
my_beetle = car.Car('volkswagen', 'beetle', 2019)
my_beetle.fill_tank()
my_beetle.drive()
my_tesla = car.ElectricCar('tesla', 'model s', 2019)
my_tesla.charge()
my_tesla.drive()
Fuel tank is full. The car is moving. The vehicle is fully charged. The car is moving.
Importing all classes from module: Don’t do this, but recognize it when you see it.
from car import *
my_beetle = Car('volkswagen', 'beetle', 2016)
my_beetle
<car.Car at 0x1da9c9e0130>
People often ask what the self variable represents. The self variable is a reference to an object that's been created from the class.
The self variable provides a way to make other variables and objects available everywhere in a class. The self variable is automatically passed to each method that's called through an object, which is why you see it listed first in every method definition. Any variable attached to self is available everywhere in the class.
The init() method is a function that's part of a class, just like any other method. The only special thing about init() is that it's called automatically every time you make a new object from a class. If you accidentally misspell init(), the method will not be called and your object may not be created correctly.
A list can hold as many items as you want, so you can make a large number of objects from a class and store them in a list.
Here's an example showing how to make a fleet of rental cars, and make sure all the cars are ready to drive.
# A fleet of rental cars
from car import Car, ElectricCar
# Make lists to hold a fleet of cars.
gas_fleet = []
electric_fleet = []
# Make 500 gas cars and 250 electric cars.
for _ in range(5):
car = Car('ford', 'escape', 2019)
gas_fleet.append(car)
for _ in range(5):
ecar = ElectricCar('nissan', 'leaf', 2019)
electric_fleet.append(ecar)
# Fill the gas cars, and charge electric cars.
for car in gas_fleet:
car.fill_tank() # Refer the Car Class methods
for ecar in electric_fleet:
ecar.charge() # Refer the ElectricCar Class methods
print(f"Gas cars: {len(gas_fleet)}")
print(f"Electric cars: {len(electric_fleet)}")
Fuel tank is full. Fuel tank is full. Fuel tank is full. Fuel tank is full. Fuel tank is full. The vehicle is fully charged. The vehicle is fully charged. The vehicle is fully charged. The vehicle is fully charged. The vehicle is fully charged. Gas cars: 5 Electric cars: 5
Your programs can read information in from files, and they can write data to files. Reading from files allows you to work with a wide variety of information; writing to files allows users to pick up where they left off the next time they run your program. You can write text to files, and you can store Python structures such as lists in data files.
Exceptions are special objects that help your programs respond to errors in appropriate ways. For example if your program tries to open a file that doesn’t exist, you can use exceptions to display an informative error message instead of having the program crash.
To read from a file your program needs to open the file and then read the contents of the file. You can read the entire contents of the file at once, or read the file line by line. The with statement makes sure the file is closed properly when the program has finished accessing the file.
# Reading an entire file at once
filename = 'siddhartha.txt'
with open(filename) as f_obj:
contents = f_obj.read()
print(contents)
This is line-1. This is line-2. This is line-3. This is line-4. This is line-5.
# Reading line by line
# Each line that's read from the file has a newline character at the
# end of the line, and the print function adds its own newline
# character. The rstrip() method gets rid of the extra blank lines
# this would result in when printing to the terminal.
filename = 'siddhartha.txt'
with open(filename) as f_obj:
for line in f_obj:
print(line.rstrip())
This is line-1. This is line-2. This is line-3. This is line-4. This is line-5.
# Storing the lines in a list
filename = 'siddhartha.txt'
with open(filename) as f_obj:
lines = f_obj.readlines()
for line in lines:
print(line.rstrip())
This is line-1. This is line-2. This is line-3. This is line-4. This is line-5.
Passing the 'w' argument to open() tells Python you want to write to the file. Be careful; this will erase the contents of the file if it already exists. Passing the 'a' argument tells Python you want to append to the end of an existing file.
# Writing to an empty file
# File is created in the directory automatically
filename = 'programming.txt'
with open(filename, 'w') as f:
f.write("I love programming!")
# Writing multiple lines to an empty file
filename = 'programming.txt'
with open(filename, 'w') as f:
f.write("I love programming!\n")
f.write("I love creating new games.\n")
# Appending to a file
filename = 'programming.txt'
with open(filename, 'a') as f:
f.write("I also love working with data.\n")
f.write("I love making apps as well.\n")
When Python runs the open() function, it looks for the file in the same directory where the program that's being executed is stored. You can open a file from a subfolder using a relative path. You can also use an absolute path to open any file on your system.
Opening a file from a subfolder
f_path = "text_files/alice.txt"
with open(f_path) as f:
lines = f.readlines()
for line in lines:
print(line.rstrip())
Opening a file using an absolute path
f_path = "/home/ehmatthes/books/alice.txt"
with open(f_path) as f:
lines = f.readlines()
Opening a file on Windows Windows will sometimes interpret forward slashes incorrectly. If you run into this, use backslashes in your file paths.
f_path = "C:\Users\ehmatthes\books\alice.txt"
with open(f_path) as f:
lines = f.readlines()
When you think an error may occur, you can write a tryexcept block to handle the exception that might be raised. The try block tells Python to try running some code, and the except block tells Python what to do if the code results in a particular kind of error.
# Handling the ZeroDivisionError exception
try:
print(5/0)
except ZeroDivisionError:
print("You can't divide by zero!")
You can't divide by zero!
# Handling the FileNotFoundError exception
f_name = 'siddhartha.txt'
try:
with open(f_name) as f:
lines = f.readlines()
for line in lines:
print(line.rstrip())
except FileNotFoundError:
msg = f"Can’t find file: {f_name}."
print(msg)
This is line-1. This is line-2. This is line-3. This is line-4. This is line-5.
It can be hard to know what kind of exception to handle when writing code. Try writing your code without a try block, and make it generate an error. The traceback will tell you what kind of exception your program needs to handle.
The try block should only contain code that may cause an error. Any code that depends on the try block running successfully should be placed in the else block.
# Using an else block
print("Enter two numbers. I'll divide them.")
x = input("First number: ")
y = input("Second number: ")
try:
result = int(x) / int(y)
except ZeroDivisionError:
print("You can't divide by zero!")
else:
print(result)
Enter two numbers. I'll divide them.
0.5
# Using an else block
print("Enter two numbers. I'll divide them.")
x = input("First number: ")
y = input("Second number: ")
try:
result = int(x) / int(y)
except ZeroDivisionError:
print("You can't divide by zero!")
else:
print(result)
Enter two numbers. I'll divide them.
You can't divide by zero!
# Preventing crashes from user input
# Without the except block in the following example, the program
# would crash if the user tries to divide by zero. As written, it will
# handle the error gracefully and keep running.
"""A simple calculator for division only."""
print("Enter two numbers. I'll divide them.")
print("Enter 'q' to quit.")
while True:
x = input("\nFirst number: ")
if x == 'q':
break
y = input("Second number: ")
if y == 'q':
break
try:
result = int(x) / int(y)
except ZeroDivisionError:
print("You can't divide by zero!")
else:
print(result)
Enter two numbers. I'll divide them. Enter 'q' to quit.
# Preventing crashes from user input
# Without the except block in the following example, the program
# would crash if the user tries to divide by zero. As written, it will
# handle the error gracefully and keep running.
"""A simple calculator for division only."""
print("Enter two numbers. I'll divide them.")
print("Enter 'q' to quit.")
while True:
x = input("\nFirst number: ")
if x == 'q':
break
y = input("Second number: ")
if y == 'q':
break
try:
result = int(x) / int(y)
except ZeroDivisionError:
print("You can't divide by zero!")
else:
print(result)
Enter two numbers. I'll divide them. Enter 'q' to quit.
You can't divide by zero!
Well-written, properly tested code is not very prone to internal errors such as syntax or logical errors. But every time your program depends on something external such as user input or the existence of a file, there's a possibility of an exception being raised.
It's up to you how to communicate errors to your users. Sometimes users need to know if a file is missing; sometimes it's better to handle the error silently. A little experience will help you know how much to report.
Sometimes you want your program to just continue running when it encounters an error, without reporting the error to the user. Using the pass statement in an else block allows you to do this.
# Using the pass statement in an else block
f_names = ['alice.txt', 'siddhartha.txt', 'moby_dick.txt', 'little_women.txt']
for f_name in f_names:
# Report the length of each file found.
try:
with open(f_name) as f:
lines = f.readlines()
except FileNotFoundError:
# Just move on to the next file.
pass
else:
num_lines = len(lines)
msg = f"{f_name} has {num_lines}"
msg += " lines."
print(msg)
siddhartha.txt has 5 lines.
Exception-handling code should catch specific exceptions that you expect to happen during your program's execution. A bare except block will catch all exceptions, including keyboard interrupts and system exits you might need when forcing a program to close.
If you want to use a try block and you're not sure which exception to catch, use Exception. It will catch most exceptions, but still allow you to interrupt programs intentionally.
Don’t use bare except blocks
try:
# Do something
except:
pass
Use Exception instead
try:
# Do something
except Exception:
pass
Printing the exception
try:
# Do something
except Exception as e:
print(e, type(e))
The json module allows you to dump simple Python data structures into a file, and load the data from that file the next time the program runs. The JSON data format is not specific to Python, so you can share this kind of data with people who work in other languages as well.
Knowing how to manage exceptions is important when working with stored data. You'll usually want to make sure the data you're trying to load exists before working with it.
# Using json.dump() to store data
"""Store some numbers."""
import json
numbers = [2, 3, 5, 7, 11, 13]
filename = 'numbers.json'
with open(filename, 'w') as f:
json.dump(numbers, f)
# Using json.load() to read data
"""Load some previously stored numbers."""
import json
filename = 'numbers.json'
with open(filename) as f:
numbers = json.load(f)
print(numbers)
[2, 3, 5, 7, 11, 13]
# Making sure the stored data exists
import json
f_name = 'numbers.json'
try:
with open(f_name) as f:
numbers = json.load(f)
except FileNotFoundError:
msg = f"Can’t find file: {f_name}."
print(msg)
else:
print(numbers)
[2, 3, 5, 7, 11, 13]
Take a program you've already written that prompts for user input, and add some error-handling code to the program.
More resources: https://docs.python.org/3/library/unittest.html
When you write a function or a class, you can also write tests for that code. Testing proves that your code works as it's supposed to in the situations it's designed to handle, and also when people use your programs in unexpected ways. Writing tests gives you confidence that your code will work correctly as more people begin to use your programs. You can also add new features to your programs and know that you haven't broken existing behavior.
A unit test verifies that one specific aspect of your code works as it's supposed to. A test case is a collection of unit tests which verify your code's behavior in a wide variety of situations.
Python's unittest module provides tools for testing your code. To try it out, we’ll create a function that returns a full name. We’ll use the function in a regular program, and then build a test case for the function.
# A function to test
# Save this as full_names.py
def get_full_name(first, last):
"""Return a full name."""
full_name = f"{first} {last}"
return full_name.title()
# Using the function
# Save this as names.py
from full_names import get_full_name
janis = get_full_name('janis', 'joplin')
print(janis)
bob = get_full_name('bob', 'dylan')
print(bob)
Janis Joplin Bob Dylan
# Building a testcase with one unit test
# To build a test case, make a class that inherits from
# unittest.TestCase and write methods that begin with test_.
# Save this as test_full_names.py
import unittest
from full_names import get_full_name
class NamesTestCase(unittest.TestCase):
"""Tests for names.py."""
def test_first_last(self):
"""Test names like Janis Joplin."""
full_name = get_full_name('janis', 'joplin')
self.assertEqual(full_name, 'Janis Joplin')
# To run the test in Notebook
# unittest.main(argv=[''], verbosity=2, exit=False)
# To run the test in Notebook
if __name__ == '__main__':
unittest.main(argv=['ignored', '-v', 'NamesTestCase'], verbosity=2, exit=False)
test_first_last (__main__.NamesTestCase) Test names like Janis Joplin. ... ok ---------------------------------------------------------------------- Ran 1 test in 0.001s OK
Python reports on each unit test in the test case. The dot reports a single passing test. Python informs us that it ran 1 test in less than 0.001 seconds, and the OK lets us know that all unit tests in the test case passed.
Failing tests are important; they tell you that a change in the code has affected existing behavior. When a test fails, you need to modify the code so the existing behavior still works.
# Modifying the function
# We’ll modify get_full_name() so it handles middle names, but
# we’ll do it in a way that breaks existing behavior.
def get_full_name(first, middle, last):
"""Return a full name."""
full_name = f"{first} {middle} {last}"
return full_name.title()
# Using the function
from full_names_fail import get_full_name
john = get_full_name('john', 'lee', 'hooker')
print(john)
david = get_full_name('david', 'lee', 'roth')
print(david)
John Lee Hooker David Lee Roth
# Running the test
# When you change your code, it’s important to run your existing
# tests. This will tell you whether the changes you made affected
# existing behavior.
import unittest
from full_names_fail import get_full_name
class NamesTestCase(unittest.TestCase):
"""Tests for names.py."""
def test_first_last(self):
"""Test names like Janis Joplin."""
full_name = get_full_name('janis', 'joplin')
self.assertEqual(full_name, 'Janis Joplin')
# To run the test in Notebook
if __name__ == '__main__':
unittest.main(argv=['ignored', '-v', 'NamesTestCase'], verbosity=2, exit=False)
test_first_last (__main__.NamesTestCase)
Test names like Janis Joplin. ... ERROR
======================================================================
ERROR: test_first_last (__main__.NamesTestCase)
Test names like Janis Joplin.
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\csoam\AppData\Local\Temp\ipykernel_13560\1667300387.py", line 13, in test_first_last
full_name = get_full_name('janis', 'joplin')
TypeError: get_full_name() missing 1 required positional argument: 'last'
----------------------------------------------------------------------
Ran 1 test in 0.002s
FAILED (errors=1)
# Fixing the code
# When a test fails, the code needs to be modified until the test
# passes again. (Don’t make the mistake of rewriting your tests to fit
# your new code.) Here we can make the middle name optional.
def get_full_name(first, last, middle=''):
"""Return a full name."""
if middle:
full_name = f"{first} {middle} {last}"
else:
full_name = f"{first} {last}"
return full_name.title()
# Running the test
# Now the test should pass again, which means our original
# functionality is still intact.
import unittest
from full_names_final import get_full_name
class NamesTestCase(unittest.TestCase):
"""Tests for names.py."""
def test_first_last(self):
"""Test names like Janis Joplin."""
full_name = get_full_name('janis', 'joplin')
self.assertEqual(full_name, 'Janis Joplin')
# To run the test in Notebook
if __name__ == '__main__':
unittest.main(argv=['ignored', '-v', 'NamesTestCase'], verbosity=2, exit=False)
test_first_last (__main__.NamesTestCase) Test names like Janis Joplin. ... ok ---------------------------------------------------------------------- Ran 1 test in 0.001s OK
You can add as many unit tests to a test case as you need. To write a new test, add a new method to your test case class.
# Testing middle names
# We’ve shown that get_full_name() works for first and last
# names. Let’s test that it works for middle names as well.
import unittest
from full_names_final import get_full_name
class NamesTestCase(unittest.TestCase):
"""Tests for names.py."""
def test_first_last(self):
"""Test names like Janis Joplin."""
full_name = get_full_name('janis', 'joplin')
self.assertEqual(full_name, 'Janis Joplin')
def test_middle(self):
"""Test names like David Lee Roth."""
full_name = get_full_name('david', 'roth', 'lee')
self.assertEqual(full_name, 'David Lee Roth')
# To run the test in Notebook-2
if __name__ == '__main__':
unittest.main(argv=['ignored', '-v', 'NamesTestCase'], verbosity=2, exit=False)
test_first_last (__main__.NamesTestCase) Test names like Janis Joplin. ... ok test_middle (__main__.NamesTestCase) Test names like David Lee Roth. ... ok ---------------------------------------------------------------------- Ran 2 tests in 0.002s OK
The two dots represent two passing tests.
Python provides a number of assert methods you can use to test your code.
Verify that a == b, or a != b
assertEqual(a, b)
assertNotEqual(a, b)
Verify that x is True, or x is False
assertTrue(x)
assertFalse(x)
Verify an item is in a list, or not in a list
assertIn(item, list)
assertNotIn(item, list)
Testing a class is similar to testing a function, since you’ll mostly be testing your methods.
# A class to test
# Save as accountant.py
class Accountant():
"""Manage a bank account."""
def __init__(self, balance=0):
self.balance = balance
def deposit(self, amount):
self.balance += amount
def withdraw(self, amount):
self.balance -= amount
# Building a testcase
# For the first test, we’ll make sure we can start out with different
# initial balances. Save this as test_accountant.py.
import unittest
from accountant import Accountant
class TestAccountant(unittest.TestCase):
"""Tests for the class Accountant."""
def test_initial_balance(self):
# Default balance should be 0.
acc = Accountant()
self.assertEqual(acc.balance, 0)
# Test non-default balance.
acc = Accountant(100)
self.assertEqual(acc.balance, 100)
# To run the test in Notebook
if __name__ == '__main__':
unittest.main(argv=['ignored', '-v', 'TestAccountant'], verbosity=2, exit=False)
test_initial_balance (__main__.TestAccountant) ... ok ---------------------------------------------------------------------- Ran 1 test in 0.001s OK
In general you shouldn’t modify a test once it’s written. When a test fails it usually means new code you’ve written has broken existing functionality, and you need to modify the new code until all existing tests pass.
If your original requirements have changed, it may be appropriate to modify some tests. This usually happens in the early stages of a project when desired behavior is still being sorted out, and no one is using your code yet.
When testing a class, you usually have to make an instance of the class. The setUp() method is run before every test. Any instances you make in setUp() are available in every test you write.
# Using setUp() to support multiple tests
# The instance self.acc can be used in each new test.
import unittest
from accountant import Accountant
class TestAccountant(unittest.TestCase):
"""Tests for the class Accountant."""
def setUp(self): # Using the setUp method
self.acc = Accountant()
def test_initial_balance(self):
# Default balance should be 0.
self.assertEqual(self.acc.balance, 0)
# Test non-default balance.
acc = Accountant(100)
self.assertEqual(acc.balance, 100)
def test_deposit(self):
# Test single deposit.
self.acc.deposit(100)
self.assertEqual(self.acc.balance, 100)
# Test multiple deposits.
self.acc.deposit(100)
self.acc.deposit(100)
self.assertEqual(self.acc.balance, 300)
def test_withdrawal(self):
# Test single withdrawal.
self.acc.deposit(1000)
self.acc.withdraw(100)
self.assertEqual(self.acc.balance, 900)
# To run the test in Notebook
if __name__ == '__main__':
unittest.main(argv=['ignored', '-v', 'TestAccountant'], verbosity=2, exit=False)
test_deposit (__main__.TestAccountant) ... ok test_initial_balance (__main__.TestAccountant) ... ok test_withdrawal (__main__.TestAccountant) ... ok ---------------------------------------------------------------------- Ran 3 tests in 0.002s OK
Data visualization involves exploring data through visual representations. The matplotlib package helps you make visually appealing representations of the data you’re working with. Matplotlib is extremely flexible; these examples will help you get started with a few simple visualizations.
Matplotlib runs on all systems, and you should be able to install it in one line.
Installing Matplotlib
! pip install matplotlib
# Making a line graph
# fig represents the entire figure, or collection of plots;
# ax represents a single plot in the figure.
import matplotlib.pyplot as plt
x_values = [0, 1, 2, 3, 4, 5]
squares = [0, 1, 4, 9, 16, 25]
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x_values, squares)
plt.show()
# Another approach
x_values = [0, 1, 2, 3, 4, 5]
squares = [0, 1, 4, 9, 16, 25]
plt.figure(figsize=(10,6))
plt.plot(x_values, squares)
plt.show()
# Making a scatter plot
# scatter() takes a list of x and y values;
# the s=10 argument controls the size of each point.
# import matplotlib.pyplot as plt
x_values = list(range(1000))
squares = [x**2 for x in x_values]
fig, ax = plt.subplots()
ax.scatter(x_values, squares, s=10)
plt.show()
Plots can be customized in a wide variety of ways. Just about any element of a plot can be customized.
# Using built-in styles
# Matplotlib comes with a number of built-in styles, which you can
# use with one additional line. The style must be specified before you
# create the figure.
# import matplotlib.pyplot as plt
x_values = list(range(1000))
squares = [x**2 for x in x_values]
# plt.style.use('seaborn')
plt.style.use("seaborn-v0_8")
fig, ax = plt.subplots()
ax.scatter(x_values, squares, s=10)
plt.show()
# seaborn styles renamed:
# Matplotlib currently ships many style files inspired from the seaborn library ("seaborn", "seaborn-bright", "seaborn-colorblind", etc.)
# but they have gone out of sync with the library itself since the release of seaborn 0.9. To prevent confusion, the style files have been
# renamed "seaborn-v0_8", "seaborn-v0_8-bright", "seaborn-v0_8-colorblind", etc. Users are encouraged to directly use seaborn to
# access the up-to-date styles.
# Seeing available styles
# You can see all available styles on your system. This can be done
# in a terminal session.
# import matplotlib.pyplot as plt
plt.style.available
['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
# Using a different style
# import matplotlib.pyplot as plt
x_values = list(range(1000))
squares = [x**2 for x in x_values]
# plt.style.use('seaborn')
plt.style.use("seaborn-v0_8-pastel")
fig, ax = plt.subplots()
ax.scatter(x_values, squares, s=10)
plt.show()
# Adding titles and labels, and scaling axes
# import matplotlib.pyplot as plt
x_values = list(range(1000))
squares = [x**2 for x in x_values]
# Set overall style to use, and plot data.
plt.style.use('seaborn-v0_8')
fig, ax = plt.subplots()
ax.scatter(x_values, squares, s=10)
# Set chart title and label axes.
ax.set_title('Square Numbers', fontsize=20)
ax.set_xlabel('Value', fontsize=14)
ax.set_ylabel('Square of Value', fontsize=14)
# Set scale of axes, and size of tick labels.
ax.axis([0, 1100, 0, 1_100_000])
ax.tick_params(axis='both', labelsize=14)
plt.show()
# Using a colormap
# A colormap varies the point colors from one shade to another,
# based on a certain value for each point. The value used to
# determine the color of each point is passed to the c argument, and
# the cmap argument specifies which colormap to use.
x_values = list(range(1000))
squares = [x**2 for x in x_values]
# Set overall style to use, and plot data.
plt.style.use('seaborn-v0_8')
fig, ax = plt.subplots()
# ax.scatter(x_values, squares, s=10)
ax.scatter(x_values, squares, c=squares, cmap=plt.cm.Blues, s=10)
# Set chart title and label axes.
ax.set_title('Square Numbers', fontsize=20)
ax.set_xlabel('Value', fontsize=14)
ax.set_ylabel('Square of Value', fontsize=14)
# Set scale of axes, and size of tick labels.
ax.axis([0, 1100, 0, 1_100_000])
ax.tick_params(axis='both', labelsize=14)
plt.show()
# Emphasizing points
# You can plot as much data as you want on one plot. Here we replot
# the first and last points larger to emphasize them.
# import matplotlib.pyplot as plt
x_values = list(range(1000))
squares = [x**2 for x in x_values]
fig, ax = plt.subplots()
ax.scatter(x_values, squares, c=squares, cmap=plt.cm.Blues, s=10)
ax.scatter(x_values[0], squares[0], c='green', s=100)
ax.scatter(x_values[-1], squares[-1], c='red', s=100)
# Set chart title and label axes.
ax.set_title('Square Numbers', fontsize=20)
ax.set_xlabel('Value', fontsize=14)
ax.set_ylabel('Square of Value', fontsize=14)
# Set scale of axes, and size of tick labels.
ax.axis([0, 1100, 0, 1_100_000])
ax.tick_params(axis='both', labelsize=14)
plt.show()
# Removing axes
# You can customize or remove axes entirely. Here’s how to access
# each axis, and hide it.
x_values = list(range(1000))
squares = [x**2 for x in x_values]
# Set overall style to use, and plot data.
plt.style.use('seaborn-v0_8')
fig, ax = plt.subplots()
# ax.scatter(x_values, squares, s=10)
ax.scatter(x_values, squares, c=squares, cmap=plt.cm.Blues, s=10)
# Set chart title and label axes.
ax.set_title('Square Numbers', fontsize=20)
ax.set_xlabel('Value', fontsize=14)
ax.set_ylabel('Square of Value', fontsize=14)
# Set scale of axes, and size of tick labels.
ax.axis([0, 1100, 0, 1_100_000])
ax.tick_params(axis='both', labelsize=14)
# Removing axes
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
# Setting a custom figure size
# You can make your plot as big or small as you want by using the
# figsize argument. The dpi argument is optional; if you don’t
# know your system’s resolution you can omit the argument and
# adjust the figsize argument accordingly.
x_values = list(range(1000))
squares = [x**2 for x in x_values]
# Set overall style to use, and plot data.
plt.style.use('seaborn-v0_8')
# fig, ax = plt.subplots()
fig, ax = plt.subplots(figsize=(10, 6), dpi=128)
# ax.scatter(x_values, squares, s=10)
ax.scatter(x_values, squares, c=squares, cmap=plt.cm.Blues, s=10)
# Set chart title and label axes.
ax.set_title('Square Numbers', fontsize=20)
ax.set_xlabel('Value', fontsize=14)
ax.set_ylabel('Square of Value', fontsize=14)
# Set scale of axes, and size of tick labels.
ax.axis([0, 1100, 0, 1_100_000])
ax.tick_params(axis='both', labelsize=14)
plt.show()
# Saving a plot
# The Matplotlib viewer has a save button, but you can also save
# your visualizations programmatically by replacing plt.show() with
# plt.savefig().
x_values = list(range(1000))
squares = [x**2 for x in x_values]
# Set overall style to use, and plot data.
plt.style.use('seaborn-v0_8')
# fig, ax = plt.subplots()
fig, ax = plt.subplots(figsize=(10, 6), dpi=128)
# ax.scatter(x_values, squares, s=10)
ax.scatter(x_values, squares, c=squares, cmap=plt.cm.Blues, s=10)
# Set chart title and label axes.
ax.set_title('Square Numbers', fontsize=20)
ax.set_xlabel('Value', fontsize=14)
ax.set_ylabel('Square of Value', fontsize=14)
# Set scale of axes, and size of tick labels.
ax.axis([0, 1100, 0, 1_100_000])
ax.tick_params(axis='both', labelsize=14)
# plt.show()
plt.savefig('squares.png', bbox_inches='tight')
You can make as many plots as you want on one figure. When you make multiple plots, you can emphasize relationships in the data. For example you can fill the space between two sets of data.
# Plotting two sets of data
# Here we use ax.scatter() twice to plot square numbers and
# cubes on the same figure.
# import matplotlib.pyplot as plt
x_values = list(range(11))
squares = [x**2 for x in x_values]
cubes = [x**3 for x in x_values]
plt.style.use('seaborn-v0_8')
fig, ax = plt.subplots()
ax.scatter(x_values, squares, c='blue', s=10)
ax.scatter(x_values, cubes, c='red', s=10)
plt.show()
# Filling the space between data sets
# The fill_between() method fills the space between two data
# sets. It takes a series of x-values and two series of y-values. It also
# takes a facecolor to use for the fill, and an optional alpha
# argument that controls the color’s transparency.
# import matplotlib.pyplot as plt
x_values = list(range(11))
squares = [x**2 for x in x_values]
cubes = [x**3 for x in x_values]
plt.style.use('seaborn-v0_8')
fig, ax = plt.subplots()
ax.scatter(x_values, squares, c='blue', s=10)
ax.scatter(x_values, cubes, c='red', s=10)
ax.fill_between(x_values, cubes, squares, facecolor='blue', alpha=0.25)
plt.show()
Many interesting data sets have a date or time as the xvalue. Python’s datetime module helps you work with this kind of data.
# Generating the current date
# The datetime.now() function returns a datetime object
# representing the current date and time.
from datetime import datetime as dt
today = dt.now()
date_string = today.strftime('%m/%d/%Y')
print(date_string)
04/15/2023
# Generating a specific date
# You can also generate a datetime object for any date and time you
# want. The positional order of arguments is year, month, and day.
# The hour, minute, second, and microsecond arguments are
# optional.
from datetime import datetime as dt
new_years = dt(2019, 1, 1)
fall_equinox = dt(year=2019, month=9, day=22)
print(new_years, fall_equinox, sep='; ')
2019-01-01 00:00:00; 2019-09-22 00:00:00
# Datetime formatting arguments
# The strptime() function generates a datetime object from a
# string, and the strftime() method generates a formatted string
# from a datetime object. The following codes let you work with dates
# exactly as you need to.
# %A Weekday name, such as Monday
# %B Month name, such as January
# %m Month, as a number (01 to 12)
# %d Day of the month, as a number (01 to 31)
# %Y Four-digit year, such as 2016
# %y Two-digit year, such as 16
# %H Hour, in 24-hour format (00 to 23)
# %I Hour, in 12-hour format (01 to 12)
# %p AM or PM
# %M Minutes (00 to 59)
# %S Seconds (00 to 61)
# Converting a string to a datetime object
new_years = dt.strptime('1/1/2019', '%m/%d/%Y')
new_years
datetime.datetime(2019, 1, 1, 0, 0)
# Converting a datetime object to a string
ny_string = new_years.strftime('%B %d, %Y')
print(ny_string)
January 01, 2019
# Plotting high temperatures
# The following code creates a list of dates and a corresponding list
# of high temperatures. It then plots the high temperatures, with the
# date labels displayed in a specific format.
# from datetime import datetime as dt
# import matplotlib.pyplot as plt
from matplotlib import dates as mdates
dates = [dt(2019, 6, 21), dt(2019, 6, 22), dt(2019, 6, 23), dt(2019, 6, 24)]
highs = [56, 57, 57, 64]
fig, ax = plt.subplots()
ax.plot(dates, highs, c='red')
ax.set_title("Daily High Temps", fontsize=24)
ax.set_ylabel("Temp (F)", fontsize=16)
x_axis = ax.get_xaxis()
x_axis.set_major_formatter(mdates.DateFormatter('%B %d %Y'))
fig.autofmt_xdate()
plt.show()
You can include as many individual graphs in one figure as you want.
# Sharing an x-axis
# The following code plots a set of squares and a set of cubes on
# two separate graphs that share a common x-axis.
# The plt.subplots() function returns a figure object and a tuple
# of axes. Each set of axes corresponds to a separate plot in the
# figure. The first two arguments control the number of rows and
# columns generated in the figure.
# import matplotlib.pyplot as plt
x_values = list(range(11))
squares = [x**2 for x in x_values]
cubes = [x**3 for x in x_values]
fig, axs = plt.subplots(2, 1, sharex=True)
axs[0].scatter(x_values, squares)
axs[0].set_title('Squares')
axs[1].scatter(x_values, cubes, c='red')
axs[1].set_title('Cubes')
plt.show()
# Sharing a y-axis
# To share a y-axis, we use the sharey=True argument.
# import matplotlib.pyplot as plt
x_values = list(range(11))
squares = [x**2 for x in x_values]
cubes = [x**3 for x in x_values]
plt.style.use('seaborn-v0_8')
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].scatter(x_values, squares)
axs[0].set_title('Squares')
axs[1].scatter(x_values, cubes, c='red')
axs[1].set_title('Cubes')
plt.show()
The matplotlib gallery and documentation are at https://matplotlib.org/. Be sure to visit the examples, gallery, and pyplot links.
Data visualization involves exploring data through visual representations. Plotly helps you make visually appealing representations of the data you’re working with. Plotly is particularly well suited for visualizations that will be presented online, because it supports interactive elements.
Plotly runs on all systems, and can be installed in one line.
Installing Plotly
! pip install matplotlib
To make a plot with Plotly, you specify the data and then pass it to a graph object. The data is stored in a list, so you can add as much data as you want to any graph.
In offline mode, the output should open automatically in a browser window.
# Import the necessaries libraries
import plotly.offline as pyo
import plotly.graph_objs as go
# Set notebook mode to work in offline
pyo.init_notebook_mode()
# Create traces
trace0 = go.Scatter(
x=[1, 2, 3, 4],
y=[10, 15, 13, 17]
)
trace1 = go.Scatter(
x=[1, 2, 3, 4],
y=[16, 5, 11, 9]
)
# Fill out data with our traces
data = [trace0, trace1]
# Plot it and save as basic-line.html
pyo.iplot(data, filename = 'basic-line')
# Making a line graph
# A line graph is a scatter plot where the points are connected.
from plotly.graph_objs import Scatter
# Define the data.
x_values = list(range(11))
squares = [x**2 for x in x_values]
# Pass the data to a graph object, and store it
# in a list.
data = [Scatter(x=x_values, y=squares)]
# Pass the data and a filename to plot().
pyo.iplot(data, filename='squares.html')
# Making a scatter plot
# To make a scatter plot, use the mode='markers' argument to tell
# Plotly to only display the markers.
# Define the data.
x_values = list(range(11))
squares = [x**2 for x in x_values]
# Pass the data to a graph object, and store it
# in a list.
# data = [Scatter(x=x_values, y=squares)]
data = [Scatter(x=x_values, y=squares, mode='markers')]
# Pass the data and a filename to plot().
pyo.iplot(data, filename='squares.html')
# Making a bar graph
# To make a bar graph, pass your data to the Bar() graph object.
from plotly.graph_objs import Bar
# Define the data.
x_values = list(range(11))
squares = [x**2 for x in x_values]
# Pass the data to a graph object, and store it
# in a list.
# data = [Scatter(x=x_values, y=squares)]
data = [Bar(x=x_values, y=squares)]
# Pass the data and a filename to plot().
pyo.iplot(data, filename='squares.html')
# Using Layout objects
# The Layout class allows you to specify titles, labels, and other
# formatting directives for your visualizations.
from plotly.graph_objs import Scatter, Layout
x_values = list(range(11))
squares = [x**2 for x in x_values]
# Add a title, and a label for each axis.
data = [Scatter(x=x_values, y=squares)]
title = 'Square Numbers'
x_axis_config = {'title': 'x'}
y_axis_config = {'title': 'Square of x'}
my_layout = Layout(title=title, xaxis=x_axis_config, yaxis=y_axis_config)
pyo.iplot({'data': data, 'layout': my_layout}, filename='squares.html')
# Data as a dictionary
# Plotly is highly customizable, and most of that flexibility comes from
# representing data and formatting directives as a dictionary. Here is
# the same data from the previous examples, defined as a dictionary.
# Defining the data as a dictionary also allows you to specify more
# information about each series. Anything that pertains to a specific
# data series such as markers, lines, and point labels, goes in the
# data dictionary. Plotly has several ways of specifying data, but
# internally all data is represented in this way.
data = [{
'type': 'scatter',
'x': x_values,
'y': squares,
'mode': 'markers',
}]
title = 'Square Numbers'
x_axis_config = {'title': 'x'}
y_axis_config = {'title': 'Square of x'}
my_layout = Layout(title=title, xaxis=x_axis_config, yaxis=y_axis_config)
pyo.iplot({'data': data, 'layout': my_layout}, filename='squares.html')
You can include as many data series as you want in a visualization. To do this, create one dictionary for each data series, and put these dictionaries in the data list. Each of these dictionaries is referred to as a trace in the Plotly documentation.
# Plotting squares and cubes
# Here we use the 'name' attribute to set the label for each
from plotly.graph_objs import Scatter
x_values = list(range(11))
squares = [x**2 for x in x_values]
cubes = [x**3 for x in x_values]
data = [
{
# Trace 1: squares
'type': 'scatter',
'x': x_values,
'y': squares,
'name': 'Squares',
},
{
# Trace 2: cubes
'type': 'scatter',
'x': x_values,
'y': cubes,
'name': 'Cubes',
},
]
pyo.iplot(data, filename='squares_cubes.html')
You can also specify the layout of your visualization as a dictionary, which gives you much more control of the overall layout.
# Layout as a dictionary
# Here is the same layout we used earlier, written as a dictionary.
# Simple elements such as the title of the chart are just key-value
# pairs. More complex elements such as axes, which can have many
# of their own settings, are nested dictionaries.
my_layout = {
'title': 'Square Numbers',
'xaxis': {
'title': 'x',
},
'yaxis': {
'title': 'Square of x',
},
}
# A more complex layout
# Here is a layout for the same data, with more specific formatting
# directives in the data and layout dictionaries.
from plotly.graph_objs import Scatter
x_values = list(range(11))
squares = [x**2 for x in x_values]
data = [{
'type': 'scatter',
'x': x_values,
'y': squares,
'mode': 'markers',
'marker': {
'size': 10,
'color': '#6688dd',
},
}]
my_layout = {
'title': 'Square Numbers',
'xaxis': {
'title': 'x',
'titlefont': {'family': 'monospace'},
},
'yaxis': {
'title': 'Square of x',
'titlefont': {'family': 'monospace'},
},
}
pyo.iplot({'data': data, 'layout': my_layout}, filename='squares.html')
# Using a colorscale
# Colorscales are often used to show variations in large datasets. In
# Plotly, colorscales are set in the marker dictionary, nested inside a
# data dictionary.
data = [{
'type': 'scatter',
'x': x_values,
'y': squares,
'mode': 'markers',
'marker': {
'colorscale': 'Viridis',
'color': squares,
'colorbar': {'title': 'Value'},
},
}]
It's often useful to have multiple plots share the same axes. This is done using the subplots module.
# Adding subplots to a figure
# To use the subplots module, you make a figure to hold all the
# charts that will be made. Then you use the add_trace() method
# to add each data series to the overall figure.
# For more help, see the documentation at
# https://plot.ly/python/subplots/.
from plotly.subplots import make_subplots
from plotly.graph_objects import Scatter
x_values = list(range(11))
squares = [x**2 for x in x_values]
cubes = [x**3 for x in x_values]
fig = make_subplots(rows=1, cols=2, shared_yaxes=True)
data = {
'type': 'scatter',
'x': x_values,
'y': squares,
}
fig.add_trace(data, row=1, col=1)
data = {
'type': 'scatter',
'x': x_values,
'y': cubes,
}
fig.add_trace(data, row=1, col=2)
pyo.iplot(fig, filename='subplots.html')
Plotly has a variety of mapping tools. For example, if you have a set of points represented by latitude and longitude, you can create a scatter plot of those points overlaying a map.
# The scattergeo chart type
# Here's a map showing the location of three of the higher peaks in
# North America. If you hover over each point, you'll see its location
# and the name of the mountain.
# Points in (lat, lon) format.
peak_coords = [
(63.069, -151.0063),
(60.5671, -140.4055),
(46.8529, -121.7604),
]
# Make matching lists of lats, lons,
# and labels.
lats = [pc[0] for pc in peak_coords]
lons = [pc[1] for pc in peak_coords]
peak_names = ['Denali', 'Mt Logan', 'Mt Rainier']
data = [{
'type': 'scattergeo',
'lon': lons,
'lat': lats,
'marker': {
'size': 10,
'color': '#227722',
},
'text': peak_names,
}]
my_layout = {
'title': 'Selected High Peaks',
'geo': {
'scope': 'north america',
'showland': True,
'showocean': True,
'showlakes': True,
'showrivers': True,
},
}
pyo.iplot({'data': data, 'layout': my_layout}, filename='peaks.html')
The Plotly documentation is extensive and well-organized. Start with the overview at https://plot.ly/python/. Here you can see an example of all the basic chart types, and click on any example to see a relevant tutorial.
Then take a look at the Python Figure Reference, at https://plot.ly/python/reference/. Make sure to click on the "How are Plotly attributes organized?" section. It's short, but really helpful.
# "%whos" - Prints All Variables in Notebook with Details (data type)
%whos
Variable Type Data/Info
----------------------------------------------
Accountant type <class 'accountant.Accountant'>
Bar type <class 'plotly.graph_objs._bar.Bar'>
Battery type <class 'car.Battery'>
Car type <class 'car.Car'>
Dog type <class '__main__.Dog'>
ElectricCar type <class 'car.ElectricCar'>
Layout type <class 'plotly.graph_objs._layout.Layout'>
NamesTestCase type <class '__main__.NamesTestCase'>
SARDog type <class '__main__.SARDog'>
Scatter type <class 'plotly.graph_objs._scatter.Scatter'>
TestAccountant type <class '__main__.TestAccountant'>
active bool False
add_fuel function <function add_fuel at 0x000001DA881FC1F0>
add_numbers function <function add_numbers at 0x000001DA88219AF0>
age int 32
age_0 int 18
age_1 int 18
ages list n=10
alien dict n=3
alien_0 dict n=1
alien_color str green
alien_num int 999999
alien_points int 0
alien_points_none NoneType None
aliens list n=1000000
ax AxesSubplot AxesSubplot(0.125,0.2;0.775x0.68)
axs ndarray 2: 2 elems, type `object`, 16 bytes
banned_users list n=4
bike str giant
bikes list n=3
bob str Bob Dylan
build_person function <function build_person at 0x000001DA9C9ABD30>
build_profile function <function build_profile at 0x000001DA8B910C10>
can_edit bool False
car Car <car.Car object at 0x000001DA9C9EF310>
city str quit
contents str This is line-1. \nThis is<...>line-4. \nThis is line-5.
copy_of_bikes list n=3
copy_of_finishers list n=5
cubes list n=11
current_number int 6
current_value int 6
data list n=1
date_string str 04/15/2023
dates list n=4
david str David Lee Roth
describe_pet function <function describe_pet at 0x000001DA8820A160>
dimension int 600
dimensions tuple n=2
dog str goblin
dogs list n=2
dt type <class 'datetime.datetime'>
ecar ElectricCar <car.ElectricCar object at 0x000001DA9CA00A60>
electric_fleet list n=5
f TextIOWrapper <_io.TextIOWrapper name='<...>de='r' encoding='cp1252'>
f_name str numbers.json
f_names list n=4
f_obj TextIOWrapper <_io.TextIOWrapper name='<...>de='r' encoding='cp1252'>
fall_equinox datetime 2019-09-22 00:00:00
fav_languages dict n=4
fav_numbers dict n=2
fig Figure Figure({\n 'data': [{'<...>wticklabels': False}}\n})
filename str numbers.json
finishers list n=5
first_bike str trek
first_name str albert
first_three list n=3
first_two list n=2
first_user str joe
full_name str marie curie
game_active bool True
gas_fleet list n=5
get_full_name function <function get_full_name at 0x000001DA9CA3E430>
go module <module 'plotly.graph_obj<...>graph_objs\\__init__.py'>
greet_user function <function greet_user at 0x000001DA8820FB80>
greet_users function <function greet_users at 0x000001DA9C9AB5E0>
group_1 list n=5
group_2 list n=5
highs list n=4
janis str Janis Joplin
john str John Lee Hooker
json module <module 'json' from 'C:\\<...>\lib\\json\\__init__.py'>
k str username
lang str haskell
langs list n=2
language str ruby
last_name str einstein
last_three list n=3
lats list n=3
line str This is line-5.
lines list n=5
location str paris
lons list n=3
make_pizza function <function make_pizza at 0x000001DA8B9090D0>
make_subplots function <function make_subplots at 0x000001DA8B0775E0>
mdates module <module 'matplotlib.dates<...>s\\matplotlib\\dates.py'>
message str quit
middle_three list n=3
most_recent_user str bea
mp function <function make_pizza at 0x000001DA8B9090D0>
msg str siddhartha.txt has 5 lines.
musician dict n=2
my_beetle Car <car.Car object at 0x000001DA9C9E0130>
my_car Car <__main__.Car object at 0x000001DA9C9E90A0>
my_dog SARDog <__main__.SARDog object at 0x000001DA891F8220>
my_ecar ElectricCar <__main__.ElectricCar obj<...>ct at 0x000001DA9C9FB3A0>
my_layout dict n=2
my_new_car Car <__main__.Car object at 0x000001DA9C9E9BE0>
my_old_car Car <__main__.Car object at 0x000001DA9C9E9820>
my_tesla ElectricCar <car.ElectricCar object at 0x000001DA891F8070>
my_truck Car <__main__.Car object at 0x000001DA9C9E9730>
name str Amit
names list n=5
new_alien dict n=4
new_user dict n=3
new_years datetime 2019-01-01 00:00:00
newest_user str ned
num_aliens int 1000000
num_lines int 5
num_responses int 4
num_tickets str b
num_users int 2
number int 5
numbers list n=6
ny_string str January 01, 2019
old_dog str hootz
old_dogs list n=2
oldest int 99
original list n=3
p module <module 'pizza' from 'C:\<...>es\\PCC Notes\\pizza.py'>
pairings dict n=5
peak_coords list n=3
peak_names list n=3
pets list n=4
pi float 3.0
pizza module <module 'pizza' from 'C:\<...>es\\PCC Notes\\pizza.py'>
player str MPS
players list n=3
plt module <module 'matplotlib.pyplo<...>\\matplotlib\\pyplot.py'>
price int 5
print_models function <function print_models at 0x000001DA8B910F70>
printed list n=3
prompt str \nAdd a player to your te<...>'quit' when you're done.
pyo module <module 'plotly.offline' <...>y\\offline\\__init__.py'>
result float 0.5
second_user str bob
square int 100
squares list n=11
sum_1 int 8
ticket_price int 15
tip float 100.0
title str Square Numbers
today datetime 2023-04-15 13:34:03.849969
topping str mushrooms
total_years int 557
trace0 Scatter Scatter({\n 'x': [1, 2<...>'y': [10, 15, 13, 17]\n})
trace1 Scatter Scatter({\n 'x': [1, 2<...>, 'y': [16, 5, 11, 9]\n})
unittest module <module 'unittest' from '<...>\\unittest\\__init__.py'>
unprinted list n=0
update_fuel_level function <function update_fuel_lev<...>el at 0x000001DA881FC430>
upper_names list n=5
user str erin
user_0 dict n=3
user_1 dict n=4
user_dict dict n=3
username str mcurie
usernames list n=3
users dict n=2
v str mcurie
x str q
x_axis XAxis XAxis(100.0,110.00000000000004)
x_axis_config dict n=1
x_values list n=11
y str 0
y_axis_config dict n=1
youngest int 1
%whos list
%whos dict
%whos function
%whos module
Variable Type Data/Info ------------------------------------- ages list n=10 aliens list n=1000000 banned_users list n=4 bikes list n=3 copy_of_bikes list n=3 copy_of_finishers list n=5 cubes list n=11 data list n=1 dates list n=4 dogs list n=2 electric_fleet list n=5 f_names list n=4 finishers list n=5 first_three list n=3 first_two list n=2 gas_fleet list n=5 group_1 list n=5 group_2 list n=5 highs list n=4 langs list n=2 last_three list n=3 lats list n=3 lines list n=5 lons list n=3 middle_three list n=3 names list n=5 numbers list n=6 old_dogs list n=2 original list n=3 peak_coords list n=3 peak_names list n=3 pets list n=4 players list n=3 printed list n=3 squares list n=11 unprinted list n=0 upper_names list n=5 usernames list n=3 x_values list n=11 Variable Type Data/Info --------------------------------- alien dict n=3 alien_0 dict n=1 fav_languages dict n=4 fav_numbers dict n=2 musician dict n=2 my_layout dict n=2 new_alien dict n=4 new_user dict n=3 pairings dict n=5 user_0 dict n=3 user_1 dict n=4 user_dict dict n=3 users dict n=2 x_axis_config dict n=1 y_axis_config dict n=1 Variable Type Data/Info ----------------------------------------- add_fuel function <function add_fuel at 0x000001DA881FC1F0> add_numbers function <function add_numbers at 0x000001DA88219AF0> build_person function <function build_person at 0x000001DA9C9ABD30> build_profile function <function build_profile at 0x000001DA8B910C10> describe_pet function <function describe_pet at 0x000001DA8820A160> get_full_name function <function get_full_name at 0x000001DA9CA3E430> greet_user function <function greet_user at 0x000001DA8820FB80> greet_users function <function greet_users at 0x000001DA9C9AB5E0> make_pizza function <function make_pizza at 0x000001DA8B9090D0> make_subplots function <function make_subplots at 0x000001DA8B0775E0> mp function <function make_pizza at 0x000001DA8B9090D0> print_models function <function print_models at 0x000001DA8B910F70> update_fuel_level function <function update_fuel_lev<...>el at 0x000001DA881FC430> Variable Type Data/Info ------------------------------ go module <module 'plotly.graph_obj<...>graph_objs\\__init__.py'> json module <module 'json' from 'C:\\<...>\lib\\json\\__init__.py'> mdates module <module 'matplotlib.dates<...>s\\matplotlib\\dates.py'> p module <module 'pizza' from 'C:\<...>es\\PCC Notes\\pizza.py'> pizza module <module 'pizza' from 'C:\<...>es\\PCC Notes\\pizza.py'> plt module <module 'matplotlib.pyplo<...>\\matplotlib\\pyplot.py'> pyo module <module 'plotly.offline' <...>y\\offline\\__init__.py'> unittest module <module 'unittest' from '<...>\\unittest\\__init__.py'>
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